Automated speech analysis for detection of cognitive and emotional changes. (31st December 2021) - König, Alexandra; Ramakers, Inez H.G.B.; Linz, Nicklas; Zeghari, Radia; Robert, Philippe
Abstract: Background: Many aspects of language and speech seem affected with increased dementia risk. Recently speech analysis has become mature enough to provide completely automatically extracted fine‐grained features highly sensitive to early cognitive and affective changes. Method: Several studies were performed during which over 150 speech samples of patients at different stages (controls, Mild Cognitive Impairment, Dementia) were collected at the clinic as well as remotely (over the phone and video‐conference system). Subsamples of patients with apathy and depression were included. The spoken features extracted from the recordings were compared against data from classical assessment tools and manual annotations. Result: Comparison to reference measures show firstly, highly accuracies in demonstrating speech differences based on clinical diagnosis (up to 90%) and secondly, significant correlations between automatically extracted and manually annotated measures ( r = 0.9). Conclusion: Speech and language features represent a promising biomarker candidate as they can be automatically and remotely extracted even over the telephone. With the help of advanced machine learning and different computational techniques the most significant markers can be identified as early indicator for screening.